In a similar line of research, Biel et al, [1]} conducted a large field experiment where U.S. Twitter users were exposed to messages from opposing political ideologies. The results from the experiment show that exposing users to opposing views actually increases political polarization. However, in that study, the messages used came from high-profile political elites. Our RDN model exposes users to messages from other, randomly selected, opinions, which might explain the differences in results. Thus, the RDN might depolarize the network precisely because it comes from a myriad of sources, not a static partisan opposite. It is worth noting that at the very extremes of the distribution, the RDN has an opposite effect on users' opinions (see Figure REF ). For these users, the average opinion they are exposed to will be farther away than for the rest of the network. In this case, we might be observing a consequence similar to the effect identified by Biel et al [1]}. Furthermore, when agents in a network were explicitly exposed to opposite opinions, with some probability \(p_{opp}\)  and no nudge \(D=0\) , we observed similar results to Biel et al (Fig. REF  d).
